INeRF: Inverting Neural Radiance Fields for Pose Estimation (2012.05877v3)

Abstract: We present iNeRF, a framework that performs mesh-free pose estimation by "inverting" a Neural RadianceField (NeRF). NeRFs have been shown to be remarkably effective for the task of view synthesis - synthesizing photorealistic novel views of real-world scenes or objects. In this work, we investigate whether we can apply analysis-by-synthesis via NeRF for mesh-free, RGB-only 6DoF pose estimation - given an image, find the translation and rotation of a camera relative to a 3D object or scene. Our method assumes that no object mesh models are available during either training or test time. Starting from an initial pose estimate, we use gradient descent to minimize the residual between pixels rendered from a NeRF and pixels in an observed image. In our experiments, we first study 1) how to sample rays during pose refinement for iNeRF to collect informative gradients and 2) how different batch sizes of rays affect iNeRF on a synthetic dataset. We then show that for complex real-world scenes from the LLFF dataset, iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF. Finally, we show iNeRF can perform category-level object pose estimation, including object instances not seen during training, with RGB images by inverting a NeRF model inferred from a single view.

• The paper introduces iNeRF, a mesh-free framework that accurately estimates 6DoF camera pose from RGB images by inverting neural radiance fields.
• It leverages gradient-based optimization and strategic ray sampling to align rendered and observed images for enhanced pose refinement.
• Experiments on synthetic and real-world datasets show that iNeRF reduces labeled data needs by up to 25%, paving the way for scalable pose estimation.

Inverting Neural Radiance Fields for Pose Estimation: A Review

The research paper titled "iNeRF: Inverting Neural Radiance Fields for Pose Estimation" presents a novel approach for estimating the pose of a camera relative to a 3D object or scene using neural radiance fields (NeRFs). This work leverages the capabilities of NeRFs, traditionally used for view synthesis, to perform mesh-free, RGB-only 6DoF pose estimation without requiring object mesh models during training or testing. The paper introduces a framework, iNeRF, which utilizes gradient-based optimization to minimize the residuals between a rendered image from a neural radiance field and an observed image.

Core Contributions and Methodology

iNeRF stands out by addressing the limitations posed by traditional pose estimation methods that demand high-quality 3D object models. Instead, iNeRF capitalizes on NeRF's ability to represent complex 3D structures from RGB images, thereby removing the dependency on mesh models. The framework operates by iteratively adjusting the camera pose estimation to align the rendered and observed images, using a trained NeRF model. This is achieved by optimizing a loss function indicative of the photometric residuals.

The paper explores various strategies for selecting rays that inform the optimization process, with a critical emphasis on the amount of rays and the sampling method used during pose refinement. The research demonstrates that the selection strategy significantly impacts the accuracy and convergence speed of the pose estimation.

Furthermore, the research explores the potential to enhance NeRF's reconstruction capabilities through iNeRF by predicting camera poses of new images, thus enabling semi-supervised learning and reducing the number of labeled images needed for training.

Experimental Evaluation

The iNeRF framework is extensively validated across synthetic data from NeRF's standard dataset and real-world complex scenes from the LLFF dataset. The experiments highlight the efficacy of iNeRF in delivering accurate pose estimates while requiring only RGB inputs. Notably, the research shows that increasing the ray batch size and employing strategic ray sampling — such as interest region sampling — substantially enhance the performance and convergence of the optimization process. Interestingly, iNeRF demonstrates its capability by improving NeRF model quality when training data is augmented with poses predicted in a semi-supervised manner, reducing the labeled data requirement by up to 25%.

Another significant aspect of the paper is its demonstration of iNeRF's capabilities in category-level pose estimation. This is shown through experiments on the ShapeNet dataset, where iNeRF competes effectively against feature-based methods without relying on object mesh models. The technique shows robustness in handling real-world data, suggesting its potential applicability in a variety of real-time scenarios, despite computational constraints.

Implications and Future Directions

The implications of this research are multifaceted. Practically, it paves the way for more accessible and scalable pose estimation systems that do not rely on resource-intensive 3D modeling processes. Theoretically, it extends the application spectrum of NeRFs beyond view synthesis, opening new avenues for integrating generative models into sensory tasks such as pose estimation. The work highlights the importance of ray sampling strategies in optimizing neural network parameters for vision tasks — an area ripe for further exploration.

Future research may focus on reducing the computational demands of iNeRF to make it feasible for real-time applications. Potential strategies could involve optimizing the rendering pipeline or integrating more advanced sampling and optimization techniques. Another promising direction could explore the incorporation of appearance variability modeling to enhance pose estimation robustness against lighting changes and occlusions.

In conclusion, iNeRF provides a compelling approach to 6DoF pose estimation by skillfully inverting a neural radiance field, offering a mesh-free alternative that's well-suited for diverse real-world applications. As this field evolves, the insights from iNeRF will likely spur further innovations in AI-driven perception and understanding of complex scenes.